Information sharing and decision coordination are central problems for large-scale product development. This paper proposes a framework for supporting a knowledge medium [32]: a computational environment in which explicitly represented knowledge serves as a communication medium among people and their programs. The framework is designed to support information sharing and coordinated communication among members of a product development organization, particularly for the tasks of design knowledge capture, dynamic notification of design changes, and active management of design dependencies. The proposed technology consists of a shared knowledge representation (language and vocabulary), protocols for foreign data encapsulation and posting to the shared environment, and mechanisms for content-directed routing of posted information to interested parties via subscription and notification services. A range of possible applications can be explored in this framework, depending on the ...

This paper describes how knowledge-based techniques can be used to overcome problems of workflow management in engineering applications. Using explicit process and product models as a basis for a workflow interpreter allows to alternate planning and execution steps, resulting in an increased flexibility of project coordination and enactment. To gain the full advantages of this flexibility, change processes have to be supported by the system. These require an improved traceability of decisions and have to be based on dependency management and change notification mechanisms. Our methods and techniques are illustrated by two applications: Urban land-use planning and software process modeling. 1 Motivation On account of global competition, the efficiency of business processes has to be improved, resulting in a reduction of process requirements in terms of time and cost. This business objective leads to approaches as lean management and business process reengineering & optimization. In ord...

. In this paper we describe a framework for defining and operationalizing conceptual models of distributed knowledge-based systems which extends published approaches by the notion of "agents" and multiple task decompositions. The main part deals with techniques underlying our distributed interpreter. We show how a client-server-architecture can be implemented which allows prototyping distributed knowledge-based systems. Further we describe our mechanism which manages task interactions and supports dependency-directed backtracking efficiently. 1 INTRODUCTION AND OVERVIEW Model-based development of knowledge-based systems is the state of the art in knowledge engineering. A well-known approach in Europe is KADS [14] which allows to model knowledge-based systems on a very high level of abstraction. For this methodology, several formal and/or operational languages were developed [5]. These do not support the distribution of tasks among several agents. Our goal is the development of a frame...

Abstract. Representations of activities dealing with the development or maintenance of software are called software process models. Process models allow for communication, reasoning, guidance, improvement, and automation. Two approaches for modeling processes and instantiating and managing the process models, namely CoMo-Kit and MVP-E, are combined to build a more powerful one. CoMo-Kit is based on AI/KE technology; it is a support tool system for general complex design processes, and was not been developed specifically with software development processes in mind. MVP-E is a process-sensitive software engineering environment for modeling and analyzing software development processes, and guides software developers. Additionally, it provides services to establish and run measurement programmes in software organizations. Because both approaches were developed independently from one another, major integration efforts had to be made to combine their both advantages. This article concentrates on the resulting language concepts, and their operationalization necessary for building automated process support.

In this paper we describe an approach which allows us to acquire, represent and managejne-grained causal depen-dencies between products. These dependencies are basi-cally derived automatically from a software process model. By representing causal dependencies and their rationales, the traceability of software development pro-cesses is improved. Our dependency-based system is able to support users in reacting on changes. Thereby, the costs of software development can be reduced. Based on the the-oretical work, the CoMo-Kit System was implemented consisting of a modeling component and a process engine. The use of the system is illustrated in the last section. 1

We propose the use of hierarchical (HTN) planning techniques to encode strategies that one or more Bots should execute while acting in highly dynamic environments such as Unreal Tournament © games. Our approach allows the formulation of a grand strategy but retains the ability of Bots to react to the events in the environment while contributing to the grand strategy.

ion 15 1.7 PARIS: Flexible Reuse of Cases at Different Levels of Abstraction Traditionally, case-based reasoning approaches retrieve, reuse, and retain cases given in a single, concrete representation. Paris 2 (Bergmann 1996; Bergmann and Wilke 1995a) is a domain independent case-based planning system that differs from this traditional approach in that it introduces abstraction techniques into the case-based reasoning process. Paris retrieves, reuses and retains cases at different (higher) levels of abstraction. In a nutshell, Paris works as follows. Available planning cases given at the concrete level are abstracted to several levels of abstraction which leads to a set of abstract cases that are stored in the case-base. Case abstraction is done automatically in the retain phase of the CBR-cycle. When a new problem must be solved, an abstract case is retrieved whose abstract problem description matches the current problem at an abstract level. In the subsequent reuse phase, the abst...

. Process support approaches use process models to allow for communication, reasoning, guidance, improvement, and automation. Two approaches are compared and combined to build a more powerful one. In this paper we describe the synthesis of the CoMo-Kit and MVP-E approaches. CoMo-Kit is based on AI/KE technology. It was developed for supporting complex design processes and is not specialized to software development. MVP-E is an environment for modeling, analyzing, and guiding which additionally provides services to establish and run measurement programmes in software organizations. Differences between both approaches are identified which are then used as alternatives when deriving requirements for process support environments. The discussion helps understand what is intrinsic to processes in general and what is special to software development processes in particular. Synthesizing both process support systems, which were developed completely independently, allows to alternate planning an...

This paper presents RepairSHOP a system capable of performing plan adaptation and plan repair. RepairSHOP is built on top of the HTN planner SHOP. RepairSHOP has three properties. The first property is its design modularity, which makes it is straightforward to apply the same process discussed in this paper to build plan adaptation capabilities in other HTN planners. Second, RepairSHOP can perform plan repair. Third, RepairSHOP takes into account failed traces during plan adaptation/repair. As a result, it can result in improvements in running time performance. We performed experiments demonstrating performance gains of plan adaptation over plan generation from the scratch, measured in CPU time for problem solving. 1

In this paper we present a novel approach for developing adaptive game AI by combining case based planning techniques and ontological knowledge from the game environment. The proposed architecture combines several components: a case-based hierarchical planner (Repair-SHOP), a bridge to connect and reason with Ontologies formalized in Description Logics (DLs) based languages (OntoBridge), a DLs reasoner (Pellet) and a framework to develop Case-Based Reasoning (CBR) systems (jCOLIBRI). In our ongoing work we are applying this approach to a commercial Civilization clone turn-based strategy game (CTP2) where game AI is in charge of planning the strategies for automated players. Our goal is to demonstrate that ontology-based retrieval will result in the retrieval of strategies that are easier to adapt than those plans returned by other classical retrieval mechanisms traditionally used in case-based planning.